451 citations · 1.7k across the 73 of their papers we have counts for
6 papers · 2 filters
The Value of Information When Deciding What to Learn
Dilip Arumugam, Benjamin Van Roy
All sequential decision-making agents explore so as to acquire knowledge about a particular target. It is often the responsibility of the agent designer to construct this target wh…
From Predictions to Decisions: The Importance of Joint Predictive Distributions
Zheng Wen, Ian Osband, Chao Qin +5
A fundamental challenge for any intelligent system is prediction: given some inputs, can you predict corresponding outcomes? Most work on supervised learning has focused on produci…
Reinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla +3
Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency poses an impediment to carrying this success over to real environ…
A Bit Better? Quantifying Information for Bandit Learning
Adithya M. Devraj, Benjamin Van Roy, Kuang Xu
The information ratio offers an approach to assessing the efficacy with which an agent balances between exploration and exploitation. Originally, this was defined to be the ratio b…
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States
Shi Dong, Benjamin Van Roy, Zhengyuan Zhou
We design a simple reinforcement learning (RL) agent that implements an optimistic version of -learning and establish through regret analysis that this agent can operate with so…
Deciding What to Learn: A Rate-Distortion Approach
Dilip Arumugam, Benjamin Van Roy
Agents that learn to select optimal actions represent a prominent focus of the sequential decision-making literature. In the face of a complex environment or constraints on time an…